In DBPN [1]}, iterative up- and down-sampling layers are provided to achieve an error feedback mechanism for projection errors at each stage. In DSRN [2]}, a dual-state recurrent network is proposed, where recurrent signals are exchanged between these states in both directions via delayed feedback. In SFRBN [3]}, a feedback block is proposed, in which the input of each iteration is the output of the previous one as the feedback information. Followed by several projection groups sequentially with dense skip connections, low-level representations are refined and become more powerful high-level representations.
